For FM we use a learning rate of \(10^{-3}\)  selected from \(\lbrace 10^{-5}, 10^{-4}, 5\times 10^{-4}, 10^{-3}, 5\times 10 ^ {-3}\rbrace \)  with leaf loss coefficient \(\lambda _T = 30\) . For A2C with entropy regularization we share parameters between the actor and critic networks, and use learning rate of \(5\times 10^{-3}\)  selected from \(\lbrace 10^{-5}, 10^{-4}, 5\times 10^{-4}, 10^{-3}, 5\times 10 ^ {-3}\rbrace \)  with entropy regularization coefficient \(5 \times 10^{-2}\)  selected from \(\lbrace 10^{-4}, 10^{-3}, 5\times 10^{-3}, 10 ^ {-2}, 5\times 10 ^ {-2}\rbrace \) . For SAC we use the formulation in [1]} with a learning rate of \(10^{-3}\)  selected from \(\lbrace 10^{-5}, 10^{-4}, 5\times 10^{-4}, 10^{-3}, 5\times 10 ^ {-3}\rbrace \) , a target network update frequency of 400 and initial random steps of 200. For the MARS baseline, we set the learning rate to \(5\times 10^{-4}\)  selected from \(\lbrace 10^{-5}, 10^{-4}, 5\times 10^{-4}, 10^{-3}, 5\times 10 ^ {-3}\rbrace \) . We run the experiments on 3 seeds and report the mean and standard error over the three runs in Table REF .
